---
title: Human-in-the-loop using server API
sidebarTitle: Human-in-the-loop using server API
---

To review, edit, and approve tool calls in an agent or workflow, use LangGraph's [human-in-the-loop](/oss/langgraph/add-human-in-the-loop) features.

## Dynamic interrupts

<Tabs>
    <Tab title="Python">
    ```python {highlight={2,34}}
    from langgraph_sdk import get_client
    from langgraph_sdk.schema import Command
    client = get_client(url=<DEPLOYMENT_URL>)

    # Using the graph deployed with the name "agent"
    assistant_id = "agent"

    # create a thread
    thread = await client.threads.create()
    thread_id = thread["thread_id"]

    # Run the graph until the interrupt is hit.
    result = await client.runs.wait(
        thread_id,
        assistant_id,
        input={"some_text": "original text"}   # (1)!
    )

    print(result['__interrupt__']) # (2)!
    # > [
    # >     {
    # >         'value': {'text_to_revise': 'original text'},
    # >         'resumable': True,
    # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
    # >         'when': 'during'
    # >     }
    # > ]


    # Resume the graph
    print(await client.runs.wait(
        thread_id,
        assistant_id,
        command=Command(resume="Edited text")   # (3)!
    ))
    # > {'some_text': 'Edited text'}
```

    1. The graph is invoked with some initial state.
    2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
        3. The graph is resumed with a `Command(resume=...)`, injecting the human's input and continuing execution.
    </Tab>
    <Tab title="JavaScript">
    ```javascript {highlight={32}}
    import { Client } from "@langchain/langgraph-sdk";
    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });

    // Using the graph deployed with the name "agent"
    const assistantID = "agent";

    // create a thread
    const thread = await client.threads.create();
    const threadID = thread["thread_id"];

    // Run the graph until the interrupt is hit.
    const result = await client.runs.wait(
      threadID,
      assistantID,
      { input: { "some_text": "original text" } }   # (1)!
    );

    console.log(result['__interrupt__']); # (2)!
    // > [
    # >     {
    # >         'value': {'text_to_revise': 'original text'},
    # >         'resumable': True,
    # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
    # >         'when': 'during'
    # >     }
    # > ]

    // Resume the graph
    console.log(await client.runs.wait(
        threadID,
        assistantID,
        { command: { resume: "Edited text" }}   # (3)!
    ));
    # > {'some_text': 'Edited text'}
```

    1. The graph is invoked with some initial state.
    2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
    3. The graph is resumed with a `{ resume: ... }` command object, injecting the human's input and continuing execution.
    </Tab>
    <Tab title="cURL">
    Create a thread:

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads \
    --header 'Content-Type: application/json' \
    --data '{}'
    ```

    Run the graph until the interrupt is hit.:

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
    --header 'Content-Type: application/json' \
    --data "{
      \"assistant_id\": \"agent\",
      \"input\": {\"some_text\": \"original text\"}
    }"
    ```

    Resume the graph:

    ```bash
    curl --request POST \
     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
     --header 'Content-Type: application/json' \
     --data "{
       \"assistant_id\": \"agent\",
       \"command\": {
         \"resume\": \"Edited text\"
       }
     }"
    ```
    </Tab>
</Tabs>

<Accordion title="Extended example: using `interrupt`">
  This is an example graph you can run in the LangGraph API server.
  See [LangGraph Platform quickstart](/langgraph-platform/deployment-quickstart) for more details.

  ```python {highlight={7,13}}
  from typing import TypedDict
  import uuid

  from langgraph.checkpoint.memory import InMemorySaver
  from langgraph.constants import START
  from langgraph.graph import StateGraph
  from langgraph.types import interrupt, Command

  class State(TypedDict):
      some_text: str

  def human_node(state: State):
      value = interrupt( # (1)!
          {
              "text_to_revise": state["some_text"] # (2)!
          }
      )
      return {
          "some_text": value # (3)!
      }


  # Build the graph
  graph_builder = StateGraph(State)
  graph_builder.add_node("human_node", human_node)
  graph_builder.add_edge(START, "human_node")

  graph = graph_builder.compile()
```

  1. `interrupt(...)` pauses execution at `human_node`, surfacing the given payload to a human.
  2. Any JSON serializable value can be passed to the `interrupt` function. Here, a dict containing the text to revise.
  3. Once resumed, the return value of `interrupt(...)` is the human-provided input, which is used to update the state.

  Once you have a running LangGraph API server, you can interact with it using
  [LangGraph SDK](/langgraph-platform/python-sdk)

    <Tabs>
        <Tab title="Python">
      ```python {highlight={2,34}}
      from langgraph_sdk import get_client
      from langgraph_sdk.schema import Command
      client = get_client(url=<DEPLOYMENT_URL>)

      # Using the graph deployed with the name "agent"
      assistant_id = "agent"

      # create a thread
      thread = await client.threads.create()
      thread_id = thread["thread_id"]

      # Run the graph until the interrupt is hit.
      result = await client.runs.wait(
          thread_id,
          assistant_id,
          input={"some_text": "original text"}   # (1)!
      )

      print(result['__interrupt__']) # (2)!
      # > [
      # >     {
      # >         'value': {'text_to_revise': 'original text'},
      # >         'resumable': True,
      # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
      # >         'when': 'during'
      # >     }
      # > ]


      # Resume the graph
      print(await client.runs.wait(
          thread_id,
          assistant_id,
          command=Command(resume="Edited text")   # (3)!
      ))
      # > {'some_text': 'Edited text'}
```

      1. The graph is invoked with some initial state.
      2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
            3. The graph is resumed with a `Command(resume=...)`, injecting the human's input and continuing execution.
        </Tab>
        <Tab title="JavaScript">
      ```javascript {highlight={32}}
      import { Client } from "@langchain/langgraph-sdk";
      const client = new Client({ apiUrl: <DEPLOYMENT_URL> });

      // Using the graph deployed with the name "agent"
      const assistantID = "agent";

      // create a thread
      const thread = await client.threads.create();
      const threadID = thread["thread_id"];

      // Run the graph until the interrupt is hit.
      const result = await client.runs.wait(
        threadID,
        assistantID,
        { input: { "some_text": "original text" } }   # (1)!
      );

      console.log(result['__interrupt__']); # (2)!
      # > [
      # >     {
      # >         'value': {'text_to_revise': 'original text'},
      # >         'resumable': True,
      # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
      # >         'when': 'during'
      # >     }
      # > ]

      // Resume the graph
      console.log(await client.runs.wait(
          threadID,
          assistantID,
          { command: { resume: "Edited text" }}   # (3)!
      ));
      # > {'some_text': 'Edited text'}
```

      1. The graph is invoked with some initial state.
      2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
      3. The graph is resumed with a `{ resume: ... }` command object, injecting the human's input and continuing execution.
        </Tab>
        <Tab title="cURL">
      Create a thread:

      ```bash
      curl --request POST \
      --url <DEPLOYMENT_URL>/threads \
      --header 'Content-Type: application/json' \
      --data '{}'
      ```

      Run the graph until the interrupt is hit:

      ```bash
      curl --request POST \
      --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
      --header 'Content-Type: application/json' \
      --data "{
        \"assistant_id\": \"agent\",
        \"input\": {\"some_text\": \"original text\"}
      }"
      ```

      Resume the graph:

      ```bash
      curl --request POST \
      --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
      --header 'Content-Type: application/json' \
      --data "{
        \"assistant_id\": \"agent\",
        \"command\": {
          \"resume\": \"Edited text\"
        }
      }"
      ```
        </Tab>
    </Tabs>
</Accordion>

## Static interrupts

Static interrupts (also known as static breakpoints) are triggered either before or after a node executes.

<Warning>
Static interrupts are **not** recommended for human-in-the-loop workflows. They are best used for debugging and testing.
</Warning>

You can set static interrupts by specifying `interrupt_before` and `interrupt_after` at compile time:

```python {highlight={1,2,3}}
graph = graph_builder.compile( # (1)!
    interrupt_before=["node_a"], # (2)!
    interrupt_after=["node_b", "node_c"], # (3)!
)
```

1. The breakpoints are set during `compile` time.
2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.

Alternatively, you can set static interrupts at run time:

<Tabs>
    <Tab title="Python">
    ```python {highlight={1,5,6}}
    await client.runs.wait( # (1)!
        thread_id,
        assistant_id,
        inputs=inputs,
        interrupt_before=["node_a"], # (2)!
        interrupt_after=["node_b", "node_c"] # (3)!
    )
```

    1. `client.runs.wait` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation.
    2. `interrupt_before` specifies the nodes where execution should pause before the node is executed.
    3. `interrupt_after` specifies the nodes where execution should pause after the node is executed.
    </Tab>
    <Tab title="JavaScript">
    ```javascript {highlight={1,6,7}}
    await client.runs.wait( // (1)!
        threadID,
        assistantID,
        {
        input: input,
        interruptBefore: ["node_a"], // (2)!
        interruptAfter: ["node_b", "node_c"] // (3)!
        }
    )
```

    1. `client.runs.wait` is called with the `interruptBefore` and `interruptAfter` parameters. This is a run-time configuration and can be changed for every invocation.
    2. `interruptBefore` specifies the nodes where execution should pause before the node is executed.
    3. `interruptAfter` specifies the nodes where execution should pause after the node is executed.
    </Tab>
    <Tab title="cURL">
    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
    --header 'Content-Type: application/json' \
    --data "{
        \"assistant_id\": \"agent\",
        \"interrupt_before\": [\"node_a\"],
        \"interrupt_after\": [\"node_b\", \"node_c\"],
        \"input\": <INPUT>
    }"
    ```
    </Tab>
</Tabs>

The following example shows how to add static interrupts:

<Tabs>
    <Tab title="Python">
    ```python
    from langgraph_sdk import get_client
    client = get_client(url=<DEPLOYMENT_URL>)

    # Using the graph deployed with the name "agent"
    assistant_id = "agent"

    # create a thread
    thread = await client.threads.create()
    thread_id = thread["thread_id"]

    # Run the graph until the breakpoint
    result = await client.runs.wait(
        thread_id,
        assistant_id,
        input=inputs   # (1)!
    )

    # Resume the graph
    await client.runs.wait(
        thread_id,
        assistant_id,
        input=None   # (2)!
    )
    ```

    1. The graph is run until the first breakpoint is hit.
    2. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit.
    </Tab>
    <Tab title="JavaScript">
    ```js
    import { Client } from "@langchain/langgraph-sdk";
    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });

    // Using the graph deployed with the name "agent"
    const assistantID = "agent";

    // create a thread
    const thread = await client.threads.create();
    const threadID = thread["thread_id"];

    // Run the graph until the breakpoint
    const result = await client.runs.wait(
      threadID,
      assistantID,
      { input: input }   # (1)!
    );

    // Resume the graph
    await client.runs.wait(
      threadID,
      assistantID,
      { input: null }   # (2)!
    );
    ```

    1. The graph is run until the first breakpoint is hit.
    2. The graph is resumed by passing in `null` for the input. This will run the graph until the next breakpoint is hit.
    </Tab>
    <Tab title="cURL">
    Create a thread:

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads \
    --header 'Content-Type: application/json' \
    --data '{}'
    ```

    Run the graph until the breakpoint:

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
    --header 'Content-Type: application/json' \
    --data "{
      \"assistant_id\": \"agent\",
      \"input\": <INPUT>
    }"
    ```

    Resume the graph:

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
    --header 'Content-Type: application/json' \
    --data "{
      \"assistant_id\": \"agent\"
    }"
    ```
    </Tab>
</Tabs>

## Learn more

* [Human-in-the-loop conceptual guide](/oss/langgraph/add-human-in-the-loop): learn more about LangGraph human-in-the-loop features.
* [Common patterns](/oss/langgraph/add-human-in-the-loop#common-patterns): learn how to implement patterns like approving/rejecting actions, requesting user input, tool call review, and validating human input.
